内弹道加速度信号误差优化方法研究
Research on Error Optimization Method of Interior Trajectory Acceleration Signal
董浩森 1边瑞卿 1张永杰 1康良伟 1胡宇 1李凯1
作者信息
- 1. 中北大学 信息探测与处理山西省重点实验室, 山西 太原 030051
- 折叠
摘要
在使用弹载存储式加速度传感器系统进行火炮内弹道的运动参数获取时,由于其内部电源纹波与传感器自身所携带干扰等原因,采集到的加速度信号携带多种噪声,导致加速度解算误差变大.为了减小加速度的解算误差,提出了基于改进自适应噪声的完备集成经验模态分解(ICEEMDAN)的加速度信号处理方法,并根据Spearman相关系数与模糊熵值的综合评价方式对模态分量进行降噪处理,使用处理后的模态分量进行加速度信号重构.对重构前后的加速度信号进行时频域分析和多种算法滤波效果对比,结果表明,信号中的噪声被有效滤除,相对于数字滤波、平滑滤波、EMD等算法,ICEEMDAN算法处理后的加速度信号的信噪比有明显提高;处理后的加速度信号解算的平均相对误差率为2.67%,相比于直接对原始信号速度解算,平均相对误差率减小了9.43%.因此在对内弹道加速度信号进行解算时,使用ICEEMDAN算法进行信号处理可以有效降低积分解算误差.
Abstract
In the process of obtaining interior ballistic motion parameters by shell-borne storage-type acceleration sensors,the acceleration signal collected carries a variety of noises due to the internal power supply ripple and interference from sensors,which leads to a large calculation error.In order to reduce the calculation error,an acceleration signal processing method based on ICEEMDAN was pro-posed.Modal components were denoised according to the Spearman correlation coefficient and fuzzy entropy comprehensive evaluation method,and the acceleration signal was reconstructed based on pro-cessed modal components.The acceleration signal before and after reconstruction was analyzed in time-frequency domain and the filtering effect of various algorithms was compared.The results show that the noise in signals is effectively filtered out,and the signal-to-noise ratio of the acceleration signal pro-cessed by ICEEMDAN algorithm is significantly improved compared with digital filtering,smooth filte-ring,EMD and other algorithms.The average relative error rate of the acceleration signal solution after processing is 2.67%,which is reduced by 9.43%compared with the original solution.Therefore,ICEEMDAN algorithm can effectively reduce the product decomposition error when solving the interior ballistic acceleration signal.
关键词
模态分解/相关系数/加速度/误差优化/内弹道Key words
mode decomposition/correlation coefficient/acceleration/error optimization/interior ballistics引用本文复制引用
基金项目
山西重点研发计划项目(202202010101007)
山西省科技成果转化引导(202204021301044)
出版年
2024
NETL
NSTL
万方数据